A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae

Abstract

By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, {mathrm{COD}}_{mathrm{inlet}}, and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, {mathrm{COD}}_{mathrm{inlet}}=600, mathrm{mg}/mathrm{l}, and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 mathrm{mg}/mathrm{l}, showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient ({R}^{2}) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively.